Integration of the Microbiome, Metabolome and Transcriptomics Data Identified Novel Metabolic Pathway Regulation in Colorectal Cancer

Distinct plasma molecular profiles between early-onset and late-onset colorectal cancer patients revealed by metabolic and lipidomic analyses

Colorectal cancer (CRC) incidence in younger adults has been steadily rising, warranting an in-depth investigation into the distinctions between early-onset CRC (EOCRC, < 50 years) and late-onset CRC (LOCRC, ≥ 50 years). Despite extensive study of clinical, pathological, and molecular traits, differentiating EOCRC from LOCRC and identifying potential biomarkers remain elusive. We analyzed plasma samples from healthy individuals, EOCRC, and LOCRC patients using liquid-chromatography mass spectrometry (LC/MS)-based metabolomics and lipidomics. Distinct polar metabolite and lipid profiles with significant metabolites altered in CRC group (e.g., choline and DG 40:4) were identified. Notably, EOCRC exhibited distinct polar metabolomic and differential lipidomic profiles compared to LOCRC, with polar metabolites like aminoadipate and uridine contributing significantly to the difference, and originating from pathways such as lysine biosynthesis and nucleotide metabolism. Furthermore, gene set enrichment analysis (GSEA) using independent TCGA gene expression data identified pathways significantly enriched in either EOCRC or LOCRC. Integrating gene expression and metabolomics data revealed numerous associations differentiating EOCRC and LOCRC. Our multi-omics integration underscores critical molecular distinctions, offers insights into the EOCRC development mechanisms and potential plasma biomarkers for diagnosis.

Composition of the colon microbiota in the individuals with inflammatory bowel disease and colon cancer

The human intestine is a habitat for microorganisms and, recently, the composition of the intestinal microbiota has been correlated with the etiology of diseases such as inflammations, sores, and tumors. Although many studies have been conducted to understand the composition of that microbiota, expanding these studies to more samples and different backgrounds will improve our knowledge. In this work, we showed the colon microbiota composition and diversity of healthy subjects, patients with inflammatory bowel disease (IBD), and colon cancer by metagenomic sequencing. Our results indicated that the relative abundance of prokaryotic and eukaryotic microbes differs between the healthy vs. tumor biopsies, tumor vs. IBD biopsies, and fresh vs. paraffin-embedded tumor biopsies. Fusobacterium, Escherichia-Shigella, and Streptococcus genera were relatively abundant in fresh tumor biopsies, while Pseudomonas was significantly elevated in IBD biopsies. Additionally, another opportunist pathogen Malasseziales was revealed as the most abundant fungal clade in IBD biopsies, especially in ulcerative colitis. We also found that, while the Basidiomycota:Ascomycota ratio was slightly lower in tumor biopsies compared to biopsies from healthy subjects, there was a significant increase in IBD biopsies. Our work will contribute to the known diversity of prokaryotic and eukaryotic microbes in the colon biopsies in patients with IBD and colon cancer.

Current Research on Molecular Biomarkers for Colorectal Cancer in Stool Samples

Colorectal cancer (CRC) is one of the most diagnosed cancers worldwide, with a high incidence and mortality rate when diagnosed late. Currently, the methods used in healthcare to diagnose CRC are the fecal occult blood test, flexible sigmoidoscopy, and colonoscopy. However, the lack of sensitivity and specificity and low population adherence are driving the need to implement other technologies that can identify biomarkers that not only help with early CRC detection but allow for the selection of more personalized treatment options. In this regard, the implementation of omics technologies, which can screen large pools of biological molecules, coupled with molecular validation, stands out as a promising tool for the discovery of new biomarkers from biopsied tissues or body fluids. This review delves into the current state of the art in the identification of novel CRC biomarkers that can distinguish cancerous tissue, specifically from fecal samples, as this could be the least invasive approach.

Genotype-microbiome-metabolome associations in early childhood, and their link to BMI and childhood obesity

The influence of genotype on defining the human gut microbiome has been extensively studied, but definite conclusions have not yet been found. To fill this knowledge gap, we leverage data from children enrolled in the Vitamin D Antenatal Asthma Reduction Trial (VDAART) from 6 months to 8 years old. We focus on a pool of 12 genes previously found to be associated with the gut microbiome in independent studies, establishing a Bonferroni corrected significance level of p-value < 2.29 × 10 -6 . We identified significant associations between SNPs in the FHIT gene (known to be associated with obesity and type 2 diabetes) and obesity-related microbiome features, and the children's BMI through their childhood. Based on these associations, we defined a set of SNPs of interest and a set of taxa of interest. Taking a multi-omics approach, we integrated plasma metabolome data into our analysis and found simultaneous associations among children's BMI, the SNPs of interest, and the taxa of interest, involving amino acids, lipids, nucleotides, and xenobiotics. Using our association results, we constructed a quadripartite graph where each disjoint node set represents SNPs in the FHIT gene, microbial taxa, plasma metabolites, or BMI measurements. Network analysis led to the discovery of patterns that identify several genetic variants, microbial taxa and metabolites as new potential markers for obesity, type 2 diabetes, or insulin resistance risk.

Interaction between intratumoral microbiota and tumor mediates the response of neoadjuvant therapy for rectal cancer

Background

Previous observations have demonstrated that the response to neoadjuvant chemoradiotherapy (nCRT) is highly variable in patients with locally advanced rectal cancer (LARC). Recent studies focusing on the intratumoral microbiota of colorectal cancer have revealed its role in oncogenesis and tumor progression. However, limited research has focused on the influence of intratumoral microbiota on the nCRT of LARC.

Methods

We explored the microbial profiles in the tumor microenvironment of LARC using RNA-seq data from a published European cohort. Microbial signatures were characterized in pathological complete response (pCR) and non-pCR groups. Multi-omics analysis was performed between intratumor microbiomes and transcriptomes.

Results

Microbial α and β diversity were significantly different in pCR and non-pCR groups. Twelve differential microbes were discovered between the pCR and non-pCR groups, six of which were related to subclusters of cancer-associated fibroblasts (CAFs) associated with extracellular matrix formation. A microbial risk score based on the relative abundance of seven differential microbes had predictive value for the nCRT response (AUC = 0.820, p < 0.001).

Conclusion

Our study presents intratumoral microbes as potential independent predictive markers for the response of nCRT to LARC and demonstrates the underlying mechanism by which the interaction between intratumoral microbes and CAFs mediates the response to nCRT.

Effects of Hypoxemia by Acute High-Altitude Exposure on Human Intestinal Flora and Metabolism

This study examined the effects of hypoxemia caused by acute high-altitude hypoxia (AHAH) exposure on the human intestinal flora and its metabolites. The changes in the intestinal flora, metabolism, and erythropoietin content in the AHAH population under altitude hypoxia conditions were comprehensively analyzed using 16S rRNA sequencing, metabonomics, and erythropoietin content. The results showed that compared with those in the control group (C group), the flora and metabolites in the hypoxemia group (D group) were altered. We found alterations in the flora according to the metabolic marker tyrosine through random forest and ROC analyses. Fecal and serum metabonomics analyses revealed that microbial metabolites could be absorbed into the blood and participate in human metabolism. Finally, a significant correlation between tyrosine and erythropoietin (EPO) content was found, which shows that human intestinal flora and its metabolites can help to confront altitude stress by regulating EPO levels. Our findings provide new insights into the adaptive mechanism and prevention of AHAH.

Distinct Signatures of Tumor-Associated Microbiota and Metabolome in Low-Grade vs. High-Grade Dysplastic Colon Polyps: Inference of Their Role in Tumor Initiation and Progression

According to the driver-passenger model for colorectal cancer (CRC), the tumor-associated microbiota is a dynamic ecosystem of bacterial species where bacteria with carcinogenic features linked to CRC initiation are defined as "drivers", while opportunistic bacteria colonizing more advanced tumor stages are known as "passengers". We reasoned that also gut microbiota-associated metabolites may be differentially enriched according to tumor stage, and be potential determinants of CRC development. Thus, we characterized the mucosa- and lumen-associated microbiota (MAM and LAM, respectively) and mucosa-associated metabolites in low- vs. high-grade dysplastic colon polyps from 78 patients. We show that MAM, obtained with a new biopsy-preserving approach, and LAM differ in composition and α/β-diversity. By stratifying patients for polyp histology, we found that bacteria proposed as passengers by previous studies colonized high-grade dysplastic adenomas, whereas driver taxa were enriched in low-grade polyps. Furthermore, we report altered "mucosa-associated metabolite" levels in low- vs. high-grade groups. Integrated microbiota-metabolome analysis suggests the involvement of the gut microbiota in the production and consumption of these metabolites. Altogether, our findings support the involvement of bacterial species and associated metabolites in CRC mucosal homeostasis in a tumor-stage-specific manner. These distinct signatures may be used to distinguish low-grade from high-grade dysplastic polyps.

Rectal swabs as a viable alternative to faecal sampling for the analysis of gut microbiota functionality and composition

Faecal or biopsy samples are frequently used to analyse the gut microbiota, but issues remain with the provision and collection of such samples. Rectal swabs are widely-utilised in clinical practice and previous data demonstrate their potential role in microbiota analyses; however, studies to date have been heterogenous, and there are a particular lack of data concerning the utility of swabs for the analysis of the microbiota's functionality and metabolome. We compared paired stool and rectal swab samples from healthy individuals to investigate whether rectal swabs are a reliable proxy for faecal sampling. There were no significant differences in key alpha and beta diversity measures between swab and faecal samples, and inter-subject variability was preserved. Additionally, no significant differences were demonstrated in abundance of major annotated phyla. Inferred gut functionality using Tax4Fun2 showed excellent correlation between the two sampling techniques (Pearson's coefficient r = 0.9217, P < 0.0001). Proton nuclear magnetic resonance (¹H NMR) spectroscopy enabled the detection of 20 metabolites, with overall excellent correlation identified between rectal swab and faecal samples for levels all metabolites collectively, although more variable degrees of association between swab and stool for levels of individual metabolites. These data support the utility of rectal swabs in both compositional and functional analyses of the gut microbiota.

Integrated analysis of intestinal microbiota and host gene expression in colorectal cancer patients

Introduction. Colorectal cancer (CRC) is one of the most common cancers and poses heavy burden on global health. The relationship between mucosal microbiome composition and colorectal gene expression are rarely studied. In this study, we integrated transcriptome data with microbiome data to investigate the relationship between them in colorectal cancer patients.Gap statement. Previous studies have identified the contribution of gut microbiota and DEGs to the pathogenesis of CRC, but the relationship between mucosal microbiome composition and colorectal gene expression are rarely studied.Aim. In this study, we integrated transcriptome data with microbiome data to investigate the relationship between mucosal microbiome composition and colorectal gene expression.Methodology. First, three independent CRC gene expression profiles (GSE184093, GSE156355 and GSE146587) from Gene Expression Omnibus (GEO) were used to identify differentially expressed genes (DEGs). Second, another dataset (GSE163366) was used to analyse gut mucosal microbiome differential abundance. GO (Gene Ontology) function and KEGG (Kyoto Encyclopaedia of Genes and Genomes) pathway enrichment analyses of the DEGs were performed. Protein-protein interactions (PPIs) of the DEGs were constructed. The Spearman correlation analysis was computed between host DEGs and gut microbiome abundance data.Results. A total of 1036 upregulated DEGs and 1194 downregulated DEGs between noncancerous tissues and cancerous tissues were identified based on the analysis. One significant module with a score 37.65 was selected out via MCODE including 41 upregulated DEGs, which are were mostly enriched in two pathways, including microtubule binding and tubulin binding. In particular, significant negative correlations are prevalent between Fusobacterium and the 41 DEGs with the correlation ranging between -0.54 and -0.35, and there commonly exist significant positive correlations between Blautia and the 41 DEGs with the correlation ranging between 0.42 and 0.54, indicating that Fusobacterium and Blautia are two of the most important microbes interacting with the gene regulation.Conclusion. Our results demonstrate significant correlation between some gut microbes and DEGs, providing a comprehensive bioinformatics analysis of them for future investigation into the molecular mechanisms and biomarkers.

Crosstalk between mucosal microbiota, host gene expression, and sociomedical factors in the progression of colorectal cancer

Various omics-based biomarkers related to the occurrence, progression, and prognosis of colorectal cancer (CRC) have been identified. In this study, we attempted to identify gut microbiome-based biomarkers and detect their association with host gene expression in the initiation and progression of CRC by integrating analysis of the gut mucosal metagenome, RNA sequencing, and sociomedical factors. We performed metagenome and RNA sequencing on colonic mucosa samples from 13 patients with advanced CRC (ACRC), 10 patients with high-risk adenoma (HRA), and 7 normal control (NC) individuals. All participants completed a questionnaire on sociomedical factors. The interaction and correlation between changes in the microbiome and gene expression were assessed using bioinformatic analysis. When comparing HRA and NC samples, which can be considered to represent the process of tumor initiation, 28 genes and five microbiome species were analyzed with correlation plots. When comparing ACRC and HRA samples, which can be considered to represent the progression of CRC, seven bacterial species and 21 genes were analyzed. When comparing ACRC and NC samples, 16 genes and five bacterial species were analyzed, and four correlation plots were generated. A network visualizing the relationship between bacterial and host gene expression in the initiation and progression of CRC indicated that Clostridium spiroforme and Tyzzerella nexilis were hub bacteria in the development and progression of CRC. Our study revealed the interactions of and correlation between the colonic mucosal microbiome and host gene expression to identify potential roles of the microbiome in the initiation and progression of CRC. Our results provide gut microbiome-based biomarkers that may be potential diagnostic markers and therapeutic targets in patients with CRC.

Dysbiosis of the Gut Microbiome Is Associated With Histopathology of Lung Cancer

Lung cancer is a malignancy with high incidence and mortality worldwide. Previous studies have shown that the gut microbiome plays an important role in the development and progression of metabolic cancers. However, data on the characteristics of the gut microbiome with different histopathology types of lung cancer remain scant. We collected stool samples from 28 healthy people (HP) and 61 lung cancer patients. The lung cancer patients were classified into three types according to their histopathology: Atypical Adenomatous Hyperplasia/Adenocarcinoma in situ (AAH/AIS), Minimally Invasive Adenocarcinoma (MIA), and Invasive Adenocarcinoma (IA). In addition, we employed 16S rRNA gene amplicon sequencing to analyze the characteristics of the gut microbiome in these patients. Our analysis revealed that the categorized cancer patients had unique intestinal flora characteristics, and had lower density and flora diversity compared to healthy people. Besides, the structure of the flora families and genera was more complex, and each group presented specific pathogenic microbiota. The patients in the AAH/AIS group and HP group had relatively similar flora structure compared with the IA and MIA groups. In addition, we identified several flora markers that showed significant changes with the development of lung cancer. Lung cancer gut microbiota showed a decrease in short-chain fatty acids (SCFAs) producing and anti-inflammatory bacteria compared to healthy people, while some pathogenic bacteria such as proinflammatory or tumor-promoting bacteria were more abundant in lung cancer patients. On the other hand, the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Clusters of Orthologous Group (COG) annotation demonstrated suppression of some dominant metabolism-related pathways in lung cancer. These findings provide new biomarkers for the diagnosis and prognostic assessment of lung cancer and lay the basis for novel targeted therapeutic strategies for the prevention and treatment of lung cancer.

CACONET: a novel classification framework for microbial correlation networks

MOTIVATION

Existing microbiome-based disease prediction relies on the ability of machine learning methods to differentiate disease from healthy subjects based on the observed taxa abundance across samples. Despite numerous microbes have been implicated as potential biomarkers, challenges remain due to not only the statistical nature of microbiome data but also the lack of understanding of microbial interactions which can be indicative of the disease.

RESULTS

We propose CACONET (classification of Compositional-Aware COrrelation NETworks), a computational framework that learns to classify microbial correlation networks and extracts potential signature interactions, taking as input taxa relative abundance across samples and their health status. By using Bayesian compositional-aware correlation inference, a collection of posterior correlation networks can be drawn and used for graph-level classification, thus incorporating uncertainty in the estimates. CACONET then employs a deep learning approach for graph classification, achieving excellent performance metrics by exploiting the correlation structure. We test the framework on both simulated data and a large real-world dataset pertaining to microbiome samples of colorectal cancer (CRC) and healthy subjects, and identify potential network substructure characteristic of CRC microbiota. CACONET is customizable and can be adapted to further improve its utility.

AVAILABILITY AND IMPLEMENTATION

CACONET is available at https://github.com/yuanwxu/corr-net-classify.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Metabolomic profiles and microbiota of GDM offspring: The key for future perspective?

Gestational diabetes mellitus (GDM), or any degree of glucose intolerance recognized for the first time during pregnancy, is one of the diseases that most frequently aggravates the course of gestation. Missed or late diagnosis and inadequate treatment are associated with high maternal and fetal morbidity, with possible short- and long-term repercussions. Estimates on the prevalence of GDM are alarming and increasing by about 30% in the last 10-20 years. In addition, there is the negative influence of the SARS-CoV-2 emergency on the glycemic control of pregnant women, making the matter increasingly topical. To date, knowledge on the metabolic maturation of newborns is still incomplete. However, in light of the considerable progress of the theory of "developmental origins of health and disease," the relevant role of the intrauterine environment cannot be overlooked. In fact, due to the high plasticity of the early stages of development, some detrimental metabolic alterations during fetal growth, including maternal hyperglycemia, are associated with a higher incidence of chronic diseases in adult life. In this context, metabolomic analysis which allows to obtain a detailed phenotypic portrait through the dynamic detection of all metabolites in cells, tissues and different biological fluids could be very useful for the early diagnosis and prevention of complications. Indeed, if the diagnostic timing is optimized through the identification of specific metabolites, the detailed understanding of the altered metabolic pathway could also allow better management and more careful monitoring, also from a nutritional profile, of the more fragile children. In this context, a further contribution derives from the analysis of the intestinal microbiota, the main responsible for the fecal metabolome, given its alteration in pregnancies complicated by GDM and the possibility of transmission to offspring. The purpose of this review is to analyze the available data regarding the alterations in the metabolomic profile and microbiota of the offspring of mothers with GDM in order to highlight future prospects for reducing GDM-related complications in children of mothers affected by this disorder.